Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations119143
Missing cells211772
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.0 MiB
Average record size in memory2.0 KiB

Variable types

Text13
Categorical6
DateTime8
Numeric17

Alerts

customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
geolocation_lat is highly overall correlated with geolocation_state and 1 other fieldsHigh correlation
geolocation_lng is highly overall correlated with geolocation_state and 1 other fieldsHigh correlation
geolocation_state is highly overall correlated with geolocation_lat and 3 other fieldsHigh correlation
payment_value is highly overall correlated with priceHigh correlation
price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with price and 3 other fieldsHigh correlation
product_width_cm is highly overall correlated with product_length_cm and 1 other fieldsHigh correlation
seller_state is highly overall correlated with geolocation_lat and 3 other fieldsHigh correlation
zip_code_prefix is highly overall correlated with geolocation_state and 1 other fieldsHigh correlation
order_status is highly imbalanced (91.6%) Imbalance
payment_type is highly imbalanced (52.6%) Imbalance
seller_state is highly imbalanced (63.3%) Imbalance
geolocation_state is highly imbalanced (62.3%) Imbalance
order_delivered_carrier_date has 2086 (1.8%) missing values Missing
order_delivered_customer_date has 3421 (2.9%) missing values Missing
review_comment_title has 105154 (88.3%) missing values Missing
review_comment_message has 68898 (57.8%) missing values Missing
product_category_name has 2542 (2.1%) missing values Missing
product_name_lenght has 2542 (2.1%) missing values Missing
product_description_lenght has 2542 (2.1%) missing values Missing
product_photos_qty has 2542 (2.1%) missing values Missing
product_category_name_english has 2567 (2.2%) missing values Missing

Reproduction

Analysis started2025-07-05 23:22:31.531273
Analysis finished2025-07-05 23:24:03.199030
Duration1 minute and 31.67 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct99441
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size10.1 MiB
2025-07-05T18:24:04.078354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3812576
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86494 ?
Unique (%)72.6%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row53cdb2fc8bc7dce0b6741e2150273451
5th row47770eb9100c2d0c44946d9cf07ec65d
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc 24
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
285c2e15bebd4ac83635ccc563dc71f4 22
 
< 0.1%
Other values (99431) 118845
99.7%
2025-07-05T18:24:05.216389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 239371
 
6.3%
b 239344
 
6.3%
6 239250
 
6.3%
e 238956
 
6.3%
3 238724
 
6.3%
c 238563
 
6.3%
8 238518
 
6.3%
7 238501
 
6.3%
1 238422
 
6.3%
a 238162
 
6.2%
Other values (6) 1424765
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 239371
 
6.3%
b 239344
 
6.3%
6 239250
 
6.3%
e 238956
 
6.3%
3 238724
 
6.3%
c 238563
 
6.3%
8 238518
 
6.3%
7 238501
 
6.3%
1 238422
 
6.3%
a 238162
 
6.2%
Other values (6) 1424765
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 239371
 
6.3%
b 239344
 
6.3%
6 239250
 
6.3%
e 238956
 
6.3%
3 238724
 
6.3%
c 238563
 
6.3%
8 238518
 
6.3%
7 238501
 
6.3%
1 238422
 
6.3%
a 238162
 
6.2%
Other values (6) 1424765
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 239371
 
6.3%
b 239344
 
6.3%
6 239250
 
6.3%
e 238956
 
6.3%
3 238724
 
6.3%
c 238563
 
6.3%
8 238518
 
6.3%
7 238501
 
6.3%
1 238422
 
6.3%
a 238162
 
6.2%
Other values (6) 1424765
37.4%
Distinct99441
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size10.1 MiB
2025-07-05T18:24:06.292081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3812576
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86494 ?
Unique (%)72.6%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowb0830fb4747a6c6d20dea0b8c802d7ef
5th row41ce2a54c0b03bf3443c3d931a367089
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d329 24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
b246eeed30b362c09d867b9e598bee51 22
 
< 0.1%
Other values (99431) 118845
99.7%
2025-07-05T18:24:07.675400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 239014
 
6.3%
2 238989
 
6.3%
5 238748
 
6.3%
c 238647
 
6.3%
6 238555
 
6.3%
1 238489
 
6.3%
8 238361
 
6.3%
d 238357
 
6.3%
7 238270
 
6.2%
a 238267
 
6.2%
Other values (6) 1426879
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 239014
 
6.3%
2 238989
 
6.3%
5 238748
 
6.3%
c 238647
 
6.3%
6 238555
 
6.3%
1 238489
 
6.3%
8 238361
 
6.3%
d 238357
 
6.3%
7 238270
 
6.2%
a 238267
 
6.2%
Other values (6) 1426879
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 239014
 
6.3%
2 238989
 
6.3%
5 238748
 
6.3%
c 238647
 
6.3%
6 238555
 
6.3%
1 238489
 
6.3%
8 238361
 
6.3%
d 238357
 
6.3%
7 238270
 
6.2%
a 238267
 
6.2%
Other values (6) 1426879
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 239014
 
6.3%
2 238989
 
6.3%
5 238748
 
6.3%
c 238647
 
6.3%
6 238555
 
6.3%
1 238489
 
6.3%
8 238361
 
6.3%
d 238357
 
6.3%
7 238270
 
6.2%
a 238267
 
6.2%
Other values (6) 1426879
37.4%

order_status
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
delivered
115723 
shipped
 
1256
canceled
 
750
unavailable
 
652
invoiced
 
378
Other values (3)
 
384

Length

Max length11
Median length9
Mean length8.9834401
Min length7

Characters and Unicode

Total characters1070314
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 115723
97.1%
shipped 1256
 
1.1%
canceled 750
 
0.6%
unavailable 652
 
0.5%
invoiced 378
 
0.3%
processing 376
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Length

2025-07-05T18:24:08.183775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T18:24:08.478985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
delivered 115723
97.1%
shipped 1256
 
1.1%
canceled 750
 
0.6%
unavailable 652
 
0.5%
invoiced 378
 
0.3%
processing 376
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1070314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1070314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1070314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%
Distinct98875
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size930.9 KiB
Minimum2016-09-04 21:15:19
Maximum2018-10-17 17:30:18
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:08.772112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:09.078147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct90733
Distinct (%)76.3%
Missing177
Missing (%)0.1%
Memory size930.9 KiB
Minimum2016-09-15 12:16:38
Maximum2018-09-03 17:40:06
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:09.446616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:09.880470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct81018
Distinct (%)69.2%
Missing2086
Missing (%)1.8%
Memory size930.9 KiB
Minimum2016-10-08 10:34:01
Maximum2018-09-11 19:48:28
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:10.280434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:10.594453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct95664
Distinct (%)82.7%
Missing3421
Missing (%)2.9%
Memory size930.9 KiB
Minimum2016-10-11 13:46:32
Maximum2018-10-17 13:22:46
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:10.956888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:11.283224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct459
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size930.9 KiB
Minimum2016-09-30 00:00:00
Maximum2018-11-12 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:11.615949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:11.866468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct96096
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size10.1 MiB
2025-07-05T18:24:12.615983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3812576
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81601 ?
Unique (%)68.5%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th rowaf07308b275d755c9edb36a90c618231
5th row3a653a41f6f9fc3d2a113cf8398680e8
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
d97b3cfb22b0d6b25ac9ed4e9c2d481b 24
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c 24
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
85963fd37bfd387aa6d915d8a1065486 24
 
< 0.1%
Other values (96086) 118820
99.7%
2025-07-05T18:24:13.911887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 239302
 
6.3%
b 238901
 
6.3%
1 238720
 
6.3%
a 238717
 
6.3%
d 238546
 
6.3%
3 238474
 
6.3%
8 238431
 
6.3%
e 238285
 
6.2%
5 238244
 
6.2%
2 238230
 
6.2%
Other values (6) 1426726
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 239302
 
6.3%
b 238901
 
6.3%
1 238720
 
6.3%
a 238717
 
6.3%
d 238546
 
6.3%
3 238474
 
6.3%
8 238431
 
6.3%
e 238285
 
6.2%
5 238244
 
6.2%
2 238230
 
6.2%
Other values (6) 1426726
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 239302
 
6.3%
b 238901
 
6.3%
1 238720
 
6.3%
a 238717
 
6.3%
d 238546
 
6.3%
3 238474
 
6.3%
8 238431
 
6.3%
e 238285
 
6.2%
5 238244
 
6.2%
2 238230
 
6.2%
Other values (6) 1426726
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3812576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 239302
 
6.3%
b 238901
 
6.3%
1 238720
 
6.3%
a 238717
 
6.3%
d 238546
 
6.3%
3 238474
 
6.3%
8 238431
 
6.3%
e 238285
 
6.2%
5 238244
 
6.2%
2 238230
 
6.2%
Other values (6) 1426726
37.4%

customer_zip_code_prefix
Real number (ℝ)

High correlation 

Distinct14994
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35033.451
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:14.255162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3275
Q111250
median24240
Q358475
95-th percentile90570
Maximum99990
Range98987
Interquartile range (IQR)47225

Descriptive statistics

Standard deviation29823.199
Coefficient of variation (CV)0.85127779
Kurtosis-0.78102574
Mean35033.451
Median Absolute Deviation (MAD)16230
Skewness0.7854738
Sum4.1739905 × 109
Variance8.894232 × 108
MonotonicityNot monotonic
2025-07-05T18:24:14.537776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 164
 
0.1%
22790 155
 
0.1%
22793 154
 
0.1%
24230 141
 
0.1%
22775 130
 
0.1%
35162 125
 
0.1%
29101 119
 
0.1%
11740 111
 
0.1%
13087 108
 
0.1%
13212 106
 
0.1%
Other values (14984) 117830
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 4
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 3
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99990 1
 
< 0.1%
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 2
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%
Distinct4119
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2025-07-05T18:24:15.339473image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.33532
Min length3

Characters and Unicode

Total characters1231381
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowbarreiras
5th rowvianopolis
ValueCountFrequency (%)
sao 25445
 
12.2%
paulo 18957
 
9.1%
de 11657
 
5.6%
rio 9967
 
4.8%
janeiro 8311
 
4.0%
do 5095
 
2.4%
belo 3373
 
1.6%
horizonte 3327
 
1.6%
brasilia 2510
 
1.2%
porto 1998
 
1.0%
Other values (3285) 118347
56.6%
2025-07-05T18:24:16.871447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1231381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1231381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1231381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

customer_state
Categorical

High correlation 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
SP
50265 
RJ
15518 
MG
13819 
RS
6573 
PR
6043 
Other values (22)
26925 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters238286
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowBA
5th rowGO

Common Values

ValueCountFrequency (%)
SP 50265
42.2%
RJ 15518
 
13.0%
MG 13819
 
11.6%
RS 6573
 
5.5%
PR 6043
 
5.1%
SC 4345
 
3.6%
BA 4091
 
3.4%
DF 2516
 
2.1%
GO 2466
 
2.1%
ES 2360
 
2.0%
Other values (17) 11147
 
9.4%

Length

2025-07-05T18:24:17.146186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 50265
42.2%
rj 15518
 
13.0%
mg 13819
 
11.6%
rs 6573
 
5.5%
pr 6043
 
5.1%
sc 4345
 
3.6%
ba 4091
 
3.4%
df 2516
 
2.1%
go 2466
 
2.1%
es 2360
 
2.0%
Other values (17) 11147
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.0947373
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:17.371155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73014099
Coefficient of variation (CV)0.66695545
Kurtosis342.28301
Mean1.0947373
Median Absolute Deviation (MAD)0
Skewness15.775506
Sum130427
Variance0.53310587
MonotonicityNot monotonic
2025-07-05T18:24:17.564931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 113999
95.7%
2 3415
 
2.9%
3 658
 
0.6%
4 322
 
0.3%
5 194
 
0.2%
6 136
 
0.1%
7 94
 
0.1%
8 63
 
0.1%
9 51
 
< 0.1%
10 42
 
< 0.1%
Other values (19) 166
 
0.1%
ValueCountFrequency (%)
1 113999
95.7%
2 3415
 
2.9%
3 658
 
0.6%
4 322
 
0.3%
5 194
 
0.2%
6 136
 
0.1%
7 94
 
0.1%
8 63
 
0.1%
9 51
 
< 0.1%
10 42
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
< 0.1%
20 6
< 0.1%

payment_type
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size7.6 MiB
credit_card
87776 
boleto
23190 
voucher
 
6465
debit_card
 
1706
not_defined
 
3

Length

Max length11
Median length11
Mean length9.7954004
Min length6

Characters and Unicode

Total characters1167024
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowboleto
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 87776
73.7%
boleto 23190
 
19.5%
voucher 6465
 
5.4%
debit_card 1706
 
1.4%
not_defined 3
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2025-07-05T18:24:17.800584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T18:24:18.010201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 87776
73.7%
boleto 23190
 
19.5%
voucher 6465
 
5.4%
debit_card 1706
 
1.4%
not_defined 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1167024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1167024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1167024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9412456
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:18.208418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7778477
Coefficient of variation (CV)0.94444604
Kurtosis2.5065453
Mean2.9412456
Median Absolute Deviation (MAD)1
Skewness1.6198199
Sum350420
Variance7.7164381
MonotonicityNot monotonic
2025-07-05T18:24:18.394380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 59446
49.9%
2 13838
 
11.6%
3 11889
 
10.0%
4 8072
 
6.8%
10 6976
 
5.9%
5 6097
 
5.1%
8 5120
 
4.3%
6 4674
 
3.9%
7 1848
 
1.6%
9 739
 
0.6%
Other values (14) 441
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 59446
49.9%
2 13838
 
11.6%
3 11889
 
10.0%
4 8072
 
6.8%
5 6097
 
5.1%
6 4674
 
3.9%
7 1848
 
1.6%
8 5120
 
4.3%
9 739
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 6
 
< 0.1%
20 21
 
< 0.1%
18 38
< 0.1%
17 8
 
< 0.1%
16 7
 
< 0.1%
15 93
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

High correlation 

Distinct29077
Distinct (%)24.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean172.73514
Minimum0
Maximum13664.08
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:18.632335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.1
Q160.85
median108.16
Q3189.24
95-th percentile515.93
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.39

Descriptive statistics

Standard deviation267.77608
Coefficient of variation (CV)1.550212
Kurtosis500.3632
Mean172.73514
Median Absolute Deviation (MAD)56.64
Skewness13.965989
Sum20579664
Variance71704.027
MonotonicityNot monotonic
2025-07-05T18:24:18.836149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 351
 
0.3%
100 300
 
0.3%
20 286
 
0.2%
77.57 250
 
0.2%
35 166
 
0.1%
73.34 160
 
0.1%
30 139
 
0.1%
116.94 133
 
0.1%
56.78 123
 
0.1%
107.78 120
 
0.1%
Other values (29067) 117112
98.3%
ValueCountFrequency (%)
0 9
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 2
 
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6922.21 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%
Distinct98410
Distinct (%)83.3%
Missing997
Missing (%)0.8%
Memory size10.1 MiB
2025-07-05T18:24:19.540427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3780672
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85411 ?
Unique (%)72.3%

Sample

1st rowa54f0611adc9ed256b57ede6b6eb5114
2nd rowa54f0611adc9ed256b57ede6b6eb5114
3rd rowa54f0611adc9ed256b57ede6b6eb5114
4th row8d5266042046a06655c8db133d120ba5
5th rowe73b67b67587f7644d5bd1a52deb1b01
ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e 63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f 38
 
< 0.1%
f28281373ab8815bafafe371218f02ce 29
 
< 0.1%
8823bba1e3301fee652eb06de8ef9435 26
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e 24
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf634 24
 
< 0.1%
b5292206f96cd5d97359940203a0b510 24
 
< 0.1%
b79b22bb50f78f1afe361661011fd892 24
 
< 0.1%
7e568736c98c553aea896a5dca746d5a 22
 
< 0.1%
e8236fe7b6e1bdd513a500de361e2b87 21
 
< 0.1%
Other values (98400) 117851
99.8%
2025-07-05T18:24:20.255795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 237115
 
6.3%
6 237048
 
6.3%
5 236734
 
6.3%
b 236598
 
6.3%
d 236586
 
6.3%
8 236562
 
6.3%
f 236490
 
6.3%
1 236390
 
6.3%
0 236298
 
6.3%
7 236091
 
6.2%
Other values (6) 1414760
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3780672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 237115
 
6.3%
6 237048
 
6.3%
5 236734
 
6.3%
b 236598
 
6.3%
d 236586
 
6.3%
8 236562
 
6.3%
f 236490
 
6.3%
1 236390
 
6.3%
0 236298
 
6.3%
7 236091
 
6.2%
Other values (6) 1414760
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3780672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 237115
 
6.3%
6 237048
 
6.3%
5 236734
 
6.3%
b 236598
 
6.3%
d 236586
 
6.3%
8 236562
 
6.3%
f 236490
 
6.3%
1 236390
 
6.3%
0 236298
 
6.3%
7 236091
 
6.2%
Other values (6) 1414760
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3780672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 237115
 
6.3%
6 237048
 
6.3%
5 236734
 
6.3%
b 236598
 
6.3%
d 236586
 
6.3%
8 236562
 
6.3%
f 236490
 
6.3%
1 236390
 
6.3%
0 236298
 
6.3%
7 236091
 
6.2%
Other values (6) 1414760
37.4%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing997
Missing (%)0.8%
Memory size6.8 MiB
5.0
66343 
4.0
22319 
1.0
15428 
3.0
9894 
2.0
 
4162

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354438
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 66343
55.7%
4.0 22319
 
18.7%
1.0 15428
 
12.9%
3.0 9894
 
8.3%
2.0 4162
 
3.5%
(Missing) 997
 
0.8%

Length

2025-07-05T18:24:20.463284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T18:24:20.622087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
5.0 66343
56.2%
4.0 22319
 
18.9%
1.0 15428
 
13.1%
3.0 9894
 
8.4%
2.0 4162
 
3.5%

Most occurring characters

ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

review_comment_title
Text

Missing 

Distinct4527
Distinct (%)32.4%
Missing105154
Missing (%)88.3%
Memory size4.3 MiB
2025-07-05T18:24:21.182801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length26
Median length20
Mean length12.213525
Min length1

Characters and Unicode

Total characters170855
Distinct characters125
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3095 ?
Unique (%)22.1%

Sample

1st rowMuito boa a loja
2nd rowNota dez
3rd rowÓtimo
4th rowNÃO RECOMENDO!!!!
5th rowNÃO RECOMENDO!!!!
ValueCountFrequency (%)
recomendo 2478
 
9.3%
produto 1570
 
5.9%
bom 1521
 
5.7%
muito 1040
 
3.9%
super 1039
 
3.9%
não 937
 
3.5%
ótimo 809
 
3.0%
excelente 769
 
2.9%
entrega 716
 
2.7%
recebi 445
 
1.7%
Other values (2100) 15445
57.7%
2025-07-05T18:24:22.017823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 21273
 
12.5%
e 18299
 
10.7%
15200
 
8.9%
r 9902
 
5.8%
t 9433
 
5.5%
a 9158
 
5.4%
m 8444
 
4.9%
d 8214
 
4.8%
i 8084
 
4.7%
n 7660
 
4.5%
Other values (115) 55188
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 170855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 21273
 
12.5%
e 18299
 
10.7%
15200
 
8.9%
r 9902
 
5.8%
t 9433
 
5.5%
a 9158
 
5.4%
m 8444
 
4.9%
d 8214
 
4.8%
i 8084
 
4.7%
n 7660
 
4.5%
Other values (115) 55188
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 170855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 21273
 
12.5%
e 18299
 
10.7%
15200
 
8.9%
r 9902
 
5.8%
t 9433
 
5.5%
a 9158
 
5.4%
m 8444
 
4.9%
d 8214
 
4.8%
i 8084
 
4.7%
n 7660
 
4.5%
Other values (115) 55188
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 170855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 21273
 
12.5%
e 18299
 
10.7%
15200
 
8.9%
r 9902
 
5.8%
t 9433
 
5.5%
a 9158
 
5.4%
m 8444
 
4.9%
d 8214
 
4.8%
i 8084
 
4.7%
n 7660
 
4.5%
Other values (115) 55188
32.3%
Distinct36159
Distinct (%)72.0%
Missing68898
Missing (%)57.8%
Memory size11.6 MiB
2025-07-05T18:24:22.760930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length208
Median length159
Mean length70.656662
Min length1

Characters and Unicode

Total characters3550144
Distinct characters209
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29556 ?
Unique (%)58.8%

Sample

1st rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2nd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3rd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
4th rowMuito bom o produto.
5th rowO produto foi exatamente o que eu esperava e estava descrito no site e chegou bem antes da data prevista.
ValueCountFrequency (%)
o 23016
 
3.8%
produto 21207
 
3.5%
e 20046
 
3.3%
a 15246
 
2.5%
de 14566
 
2.4%
não 13436
 
2.2%
do 13109
 
2.2%
que 10560
 
1.7%
prazo 9427
 
1.6%
muito 9086
 
1.5%
Other values (19737) 456676
75.3%
2025-07-05T18:24:23.701811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
562672
15.8%
o 350271
 
9.9%
e 340009
 
9.6%
a 282074
 
7.9%
r 200163
 
5.6%
i 163758
 
4.6%
t 161344
 
4.5%
d 150134
 
4.2%
n 138033
 
3.9%
s 134085
 
3.8%
Other values (199) 1067601
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3550144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
562672
15.8%
o 350271
 
9.9%
e 340009
 
9.6%
a 282074
 
7.9%
r 200163
 
5.6%
i 163758
 
4.6%
t 161344
 
4.5%
d 150134
 
4.2%
n 138033
 
3.9%
s 134085
 
3.8%
Other values (199) 1067601
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3550144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
562672
15.8%
o 350271
 
9.9%
e 340009
 
9.6%
a 282074
 
7.9%
r 200163
 
5.6%
i 163758
 
4.6%
t 161344
 
4.5%
d 150134
 
4.2%
n 138033
 
3.9%
s 134085
 
3.8%
Other values (199) 1067601
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3550144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
562672
15.8%
o 350271
 
9.9%
e 340009
 
9.6%
a 282074
 
7.9%
r 200163
 
5.6%
i 163758
 
4.6%
t 161344
 
4.5%
d 150134
 
4.2%
n 138033
 
3.9%
s 134085
 
3.8%
Other values (199) 1067601
30.1%
Distinct636
Distinct (%)0.5%
Missing997
Missing (%)0.8%
Memory size930.9 KiB
Minimum2016-10-02 00:00:00
Maximum2018-08-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:23.922663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:24.124219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct98248
Distinct (%)83.2%
Missing997
Missing (%)0.8%
Memory size930.9 KiB
Minimum2016-10-07 18:32:28
Maximum2018-10-29 12:27:35
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:24.325509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:24.548096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.196543
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:24.735484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6994889
Coefficient of variation (CV)0.58459154
Kurtosis103.35482
Mean1.196543
Median Absolute Deviation (MAD)0
Skewness7.5517266
Sum141563
Variance0.48928472
MonotonicityNot monotonic
2025-07-05T18:24:24.922883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 103645
87.0%
2 10317
 
8.7%
3 2396
 
2.0%
4 995
 
0.8%
5 472
 
0.4%
6 265
 
0.2%
7 61
 
0.1%
8 37
 
< 0.1%
9 29
 
< 0.1%
10 26
 
< 0.1%
Other values (11) 67
 
0.1%
(Missing) 833
 
0.7%
ValueCountFrequency (%)
1 103645
87.0%
2 10317
 
8.7%
3 2396
 
2.0%
4 995
 
0.8%
5 472
 
0.4%
6 265
 
0.2%
7 61
 
0.1%
8 37
 
< 0.1%
9 29
 
< 0.1%
10 26
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
< 0.1%
19 3
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 5
 
< 0.1%
14 7
< 0.1%
13 8
< 0.1%
12 13
< 0.1%
Distinct32951
Distinct (%)27.9%
Missing833
Missing (%)0.7%
Memory size10.1 MiB
2025-07-05T18:24:25.313148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3785920
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17345 ?
Unique (%)14.7%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row595fac2a385ac33a80bd5114aec74eb8
5th rowaa4383b373c6aca5d8797843e5594415
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 536
 
0.5%
99a4788cb24856965c36a24e339b6058 528
 
0.4%
422879e10f46682990de24d770e7f83d 508
 
0.4%
389d119b48cf3043d311335e499d9c6b 406
 
0.3%
368c6c730842d78016ad823897a372db 398
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 391
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 357
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 327
 
0.3%
154e7e31ebfa092203795c972e5804a6 295
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7 278
 
0.2%
Other values (32941) 114286
96.6%
2025-07-05T18:24:25.912108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 243128
 
6.4%
9 241092
 
6.4%
e 238897
 
6.3%
8 238246
 
6.3%
7 238157
 
6.3%
4 237487
 
6.3%
a 237289
 
6.3%
c 236394
 
6.2%
0 236277
 
6.2%
2 236110
 
6.2%
Other values (6) 1402843
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3785920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 243128
 
6.4%
9 241092
 
6.4%
e 238897
 
6.3%
8 238246
 
6.3%
7 238157
 
6.3%
4 237487
 
6.3%
a 237289
 
6.3%
c 236394
 
6.2%
0 236277
 
6.2%
2 236110
 
6.2%
Other values (6) 1402843
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3785920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 243128
 
6.4%
9 241092
 
6.4%
e 238897
 
6.3%
8 238246
 
6.3%
7 238157
 
6.3%
4 237487
 
6.3%
a 237289
 
6.3%
c 236394
 
6.2%
0 236277
 
6.2%
2 236110
 
6.2%
Other values (6) 1402843
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3785920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 243128
 
6.4%
9 241092
 
6.4%
e 238897
 
6.3%
8 238246
 
6.3%
7 238157
 
6.3%
4 237487
 
6.3%
a 237289
 
6.3%
c 236394
 
6.2%
0 236277
 
6.2%
2 236110
 
6.2%
Other values (6) 1402843
37.1%
Distinct3095
Distinct (%)2.6%
Missing833
Missing (%)0.7%
Memory size10.1 MiB
2025-07-05T18:24:26.282653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3785920
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique487 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row289cdb325fb7e7f891c38608bf9e0962
5th row4869f7a5dfa277a7dca6462dcf3b52b2
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2155
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2130
 
1.8%
1f50f920176fa81dab994f9023523100 2017
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1893
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1662
 
1.4%
955fee9216a65b617aa5c0531780ce60 1530
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1477
 
1.2%
7c67e1448b00f6e969d365cea6b010ab 1463
 
1.2%
7a67c85e85bb2ce8582c35f2203ad736 1245
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1240
 
1.0%
Other values (3085) 101498
85.8%
2025-07-05T18:24:26.867211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 256833
 
6.8%
c 250006
 
6.6%
4 248481
 
6.6%
6 243514
 
6.4%
0 242843
 
6.4%
a 241350
 
6.4%
b 240801
 
6.4%
3 240746
 
6.4%
9 235027
 
6.2%
2 233698
 
6.2%
Other values (6) 1352621
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3785920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 256833
 
6.8%
c 250006
 
6.6%
4 248481
 
6.6%
6 243514
 
6.4%
0 242843
 
6.4%
a 241350
 
6.4%
b 240801
 
6.4%
3 240746
 
6.4%
9 235027
 
6.2%
2 233698
 
6.2%
Other values (6) 1352621
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3785920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 256833
 
6.8%
c 250006
 
6.6%
4 248481
 
6.6%
6 243514
 
6.4%
0 242843
 
6.4%
a 241350
 
6.4%
b 240801
 
6.4%
3 240746
 
6.4%
9 235027
 
6.2%
2 233698
 
6.2%
Other values (6) 1352621
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3785920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 256833
 
6.8%
c 250006
 
6.6%
4 248481
 
6.6%
6 243514
 
6.4%
0 242843
 
6.4%
a 241350
 
6.4%
b 240801
 
6.4%
3 240746
 
6.4%
9 235027
 
6.2%
2 233698
 
6.2%
Other values (6) 1352621
35.7%
Distinct93318
Distinct (%)78.9%
Missing833
Missing (%)0.7%
Memory size930.9 KiB
Minimum2016-09-19 00:15:34
Maximum2020-04-09 22:35:08
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T18:24:27.100384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:24:27.337619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation 

Distinct5968
Distinct (%)5.0%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean120.6466
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:27.631938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.10969
Coefficient of variation (CV)1.5260247
Kurtosis119.15494
Mean120.6466
Median Absolute Deviation (MAD)42
Skewness7.8925735
Sum14273700
Variance33896.378
MonotonicityNot monotonic
2025-07-05T18:24:27.963835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2619
 
2.2%
69.9 2113
 
1.8%
49.9 2051
 
1.7%
89.9 1644
 
1.4%
99.9 1526
 
1.3%
39.9 1403
 
1.2%
29.9 1387
 
1.2%
19.9 1284
 
1.1%
79.9 1282
 
1.1%
29.99 1228
 
1.0%
Other values (5958) 101773
85.4%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6999
Distinct (%)5.9%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean20.032387
Minimum0
Maximum409.68
Zeros390
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:28.301475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.28
Q321.18
95-th percentile45.3
Maximum409.68
Range409.68
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation15.83685
Coefficient of variation (CV)0.79056234
Kurtosis57.635327
Mean20.032387
Median Absolute Deviation (MAD)3.63
Skewness5.5433839
Sum2370031.6
Variance250.80583
MonotonicityNot monotonic
2025-07-05T18:24:28.565190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3861
 
3.2%
7.78 2355
 
2.0%
11.85 1999
 
1.7%
14.1 1992
 
1.7%
18.23 1632
 
1.4%
7.39 1573
 
1.3%
16.11 1211
 
1.0%
15.23 1064
 
0.9%
8.72 970
 
0.8%
16.79 930
 
0.8%
Other values (6989) 100723
84.5%
ValueCountFrequency (%)
0 390
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 9
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

product_category_name
Text

Missing 

Distinct73
Distinct (%)0.1%
Missing2542
Missing (%)2.1%
Memory size8.1 MiB
2025-07-05T18:24:28.956239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length46
Median length32
Mean length14.876202
Min length3

Characters and Unicode

Total characters1734580
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutilidades_domesticas
2nd rowutilidades_domesticas
3rd rowutilidades_domesticas
4th rowperfumaria
5th rowautomotivo
ValueCountFrequency (%)
cama_mesa_banho 11988
 
10.3%
beleza_saude 10032
 
8.6%
esporte_lazer 9004
 
7.7%
moveis_decoracao 8832
 
7.6%
informatica_acessorios 8150
 
7.0%
utilidades_domesticas 7380
 
6.3%
relogios_presentes 6213
 
5.3%
telefonia 4726
 
4.1%
ferramentas_jardim 4590
 
3.9%
automotivo 4400
 
3.8%
Other values (63) 41286
35.4%
2025-07-05T18:24:29.534536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 210541
12.1%
a 208834
12.0%
s 172854
10.0%
o 171612
9.9%
i 115171
 
6.6%
r 111455
 
6.4%
_ 110493
 
6.4%
t 83288
 
4.8%
c 82435
 
4.8%
m 78683
 
4.5%
Other values (18) 389214
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1734580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 210541
12.1%
a 208834
12.0%
s 172854
10.0%
o 171612
9.9%
i 115171
 
6.6%
r 111455
 
6.4%
_ 110493
 
6.4%
t 83288
 
4.8%
c 82435
 
4.8%
m 78683
 
4.5%
Other values (18) 389214
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1734580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 210541
12.1%
a 208834
12.0%
s 172854
10.0%
o 171612
9.9%
i 115171
 
6.6%
r 111455
 
6.4%
_ 110493
 
6.4%
t 83288
 
4.8%
c 82435
 
4.8%
m 78683
 
4.5%
Other values (18) 389214
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1734580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 210541
12.1%
a 208834
12.0%
s 172854
10.0%
o 171612
9.9%
i 115171
 
6.6%
r 111455
 
6.4%
_ 110493
 
6.4%
t 83288
 
4.8%
c 82435
 
4.8%
m 78683
 
4.5%
Other values (18) 389214
22.4%

product_name_lenght
Real number (ℝ)

Missing 

Distinct66
Distinct (%)0.1%
Missing2542
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean48.767498
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:29.765432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.03354
Coefficient of variation (CV)0.20574236
Kurtosis0.14950788
Mean48.767498
Median Absolute Deviation (MAD)6
Skewness-0.90489402
Sum5686339
Variance100.67193
MonotonicityNot monotonic
2025-07-05T18:24:29.992427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8679
 
7.3%
60 8070
 
6.8%
56 6847
 
5.7%
58 6819
 
5.7%
57 6302
 
5.3%
55 5833
 
4.9%
54 5529
 
4.6%
53 4357
 
3.7%
52 4328
 
3.6%
49 3690
 
3.1%
Other values (56) 56147
47.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 15
 
< 0.1%
10 9
 
< 0.1%
11 11
 
< 0.1%
12 38
< 0.1%
13 26
< 0.1%
14 47
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
64 174
 
0.1%
63 1350
1.1%
62 167
 
0.1%
61 241
 
0.2%

product_description_lenght
Real number (ℝ)

Missing 

Distinct2960
Distinct (%)2.5%
Missing2542
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean785.96782
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:30.273815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1346
median600
Q3983
95-th percentile2123
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation652.58412
Coefficient of variation (CV)0.83029369
Kurtosis4.9299321
Mean785.96782
Median Absolute Deviation (MAD)296
Skewness2.0121562
Sum91644634
Variance425866.03
MonotonicityNot monotonic
2025-07-05T18:24:30.528291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 711
 
0.6%
1893 667
 
0.6%
348 648
 
0.5%
492 594
 
0.5%
903 594
 
0.5%
245 587
 
0.5%
366 537
 
0.5%
236 516
 
0.4%
340 487
 
0.4%
919 442
 
0.4%
Other values (2950) 110818
93.0%
(Missing) 2542
 
2.1%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 4
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Missing 

Distinct19
Distinct (%)< 0.1%
Missing2542
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.2051612
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:30.731383image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7174519
Coefficient of variation (CV)0.7788328
Kurtosis4.8200793
Mean2.2051612
Median Absolute Deviation (MAD)0
Skewness1.9087497
Sum257124
Variance2.9496409
MonotonicityNot monotonic
2025-07-05T18:24:30.936145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 58957
49.5%
2 23054
 
19.3%
3 12978
 
10.9%
4 8863
 
7.4%
5 5599
 
4.7%
6 3945
 
3.3%
7 1560
 
1.3%
8 774
 
0.6%
10 354
 
0.3%
9 318
 
0.3%
Other values (9) 199
 
0.2%
(Missing) 2542
 
2.1%
ValueCountFrequency (%)
1 58957
49.5%
2 23054
 
19.3%
3 12978
 
10.9%
4 8863
 
7.4%
5 5599
 
4.7%
6 3945
 
3.3%
7 1560
 
1.3%
8 774
 
0.6%
9 318
 
0.3%
10 354
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 60
 
0.1%
11 73
 
0.1%
10 354
0.3%

product_weight_g
Real number (ℝ)

High correlation 

Distinct2204
Distinct (%)1.9%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2112.2507
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:31.246033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3786.6951
Coefficient of variation (CV)1.7927299
Kurtosis16.01826
Mean2112.2507
Median Absolute Deviation (MAD)500
Skewness3.5830918
Sum2.4985814 × 108
Variance14339060
MonotonicityNot monotonic
2025-07-05T18:24:31.549926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7093
 
6.0%
150 5410
 
4.5%
250 4741
 
4.0%
300 4429
 
3.7%
400 3787
 
3.2%
100 3666
 
3.1%
350 3291
 
2.8%
500 2856
 
2.4%
600 2838
 
2.4%
700 2148
 
1.8%
Other values (2194) 78031
65.5%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 991
0.8%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 303
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean30.265145
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:31.860829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.189367
Coefficient of variation (CV)0.53491788
Kurtosis3.6785662
Mean30.265145
Median Absolute Deviation (MAD)8
Skewness1.7456849
Sum3580064
Variance262.09561
MonotonicityNot monotonic
2025-07-05T18:24:32.132443image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 18363
 
15.4%
20 10999
 
9.2%
30 7951
 
6.7%
17 6202
 
5.2%
18 5909
 
5.0%
19 4898
 
4.1%
25 4871
 
4.1%
40 4360
 
3.7%
22 4000
 
3.4%
50 3163
 
2.7%
Other values (89) 47574
39.9%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
 
0.1%
12 41
 
< 0.1%
13 60
 
0.1%
14 138
 
0.1%
15 220
 
0.2%
16 18363
15.4%
ValueCountFrequency (%)
105 335
0.3%
104 35
 
< 0.1%
103 46
 
< 0.1%
102 60
 
0.1%
101 108
 
0.1%
100 429
0.4%
99 36
 
< 0.1%
98 50
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

High correlation 

Distinct102
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean16.619706
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:32.374280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.453584
Coefficient of variation (CV)0.80949592
Kurtosis7.2778781
Mean16.619706
Median Absolute Deviation (MAD)6
Skewness2.2389625
Sum1965945
Variance180.99892
MonotonicityNot monotonic
2025-07-05T18:24:32.640651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10374
 
8.7%
20 6915
 
5.8%
15 6896
 
5.8%
12 6520
 
5.5%
11 6432
 
5.4%
2 5254
 
4.4%
4 4910
 
4.1%
8 4873
 
4.1%
5 4776
 
4.0%
16 4765
 
4.0%
Other values (92) 56575
47.5%
ValueCountFrequency (%)
2 5254
4.4%
3 2821
 
2.4%
4 4910
4.1%
5 4776
4.0%
6 3576
 
3.0%
7 4387
3.7%
8 4873
4.1%
9 3408
 
2.9%
10 10374
8.7%
11 6432
5.4%
ValueCountFrequency (%)
105 139
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 43
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean23.074799
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:32.904103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.749139
Coefficient of variation (CV)0.50917622
Kurtosis4.5530162
Mean23.074799
Median Absolute Deviation (MAD)6
Skewness1.707171
Sum2729518
Variance138.04227
MonotonicityNot monotonic
2025-07-05T18:24:33.132266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12701
 
10.7%
11 11144
 
9.4%
15 9376
 
7.9%
16 8810
 
7.4%
30 8045
 
6.8%
12 5711
 
4.8%
13 5491
 
4.6%
14 4846
 
4.1%
18 4192
 
3.5%
40 4157
 
3.5%
Other values (85) 43817
36.8%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
 
< 0.1%
10 83
 
0.1%
11 11144
9.4%
12 5711
4.8%
13 5491
4.6%
14 4846
4.1%
15 9376
7.9%
ValueCountFrequency (%)
118 8
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 43
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%

zip_code_prefix
Real number (ℝ)

High correlation 

Distinct2246
Distinct (%)1.9%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean24442.41
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.9 KiB
2025-07-05T18:24:33.408908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2972
Q16429
median13660
Q327972
95-th percentile88308
Maximum99730
Range98729
Interquartile range (IQR)21543

Descriptive statistics

Standard deviation27573.005
Coefficient of variation (CV)1.1280804
Kurtosis0.93697995
Mean24442.41
Median Absolute Deviation (MAD)8123
Skewness1.5561361
Sum2.8917816 × 109
Variance7.6027058 × 108
MonotonicityNot monotonic
2025-07-05T18:24:34.851386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14940 8373
 
7.0%
5849 2145
 
1.8%
15025 2098
 
1.8%
9015 1899
 
1.6%
13405 1678
 
1.4%
8577 1556
 
1.3%
4782 1549
 
1.3%
3204 1477
 
1.2%
4160 1268
 
1.1%
13232 1255
 
1.1%
Other values (2236) 95012
79.7%
ValueCountFrequency (%)
1001 22
 
< 0.1%
1021 41
 
< 0.1%
1022 5
 
< 0.1%
1023 5
 
< 0.1%
1026 323
0.3%
1031 129
 
0.1%
1035 18
 
< 0.1%
1039 1
 
< 0.1%
1040 25
 
< 0.1%
1041 2
 
< 0.1%
ValueCountFrequency (%)
99730 12
 
< 0.1%
99700 2
 
< 0.1%
99670 1
 
< 0.1%
99500 61
0.1%
99300 2
 
< 0.1%
98975 22
 
< 0.1%
98920 2
 
< 0.1%
98910 14
 
< 0.1%
98803 66
0.1%
98780 4
 
< 0.1%
Distinct611
Distinct (%)0.5%
Missing833
Missing (%)0.7%
Memory size7.6 MiB
2025-07-05T18:24:35.589361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length40
Median length31
Mean length10.102451
Min length2

Characters and Unicode

Total characters1195221
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowmaua
2nd rowmaua
3rd rowmaua
4th rowbelo horizonte
5th rowguariba
ValueCountFrequency (%)
sao 36362
 
17.9%
paulo 29574
 
14.5%
ibitinga 8373
 
4.1%
rio 5930
 
2.9%
do 5524
 
2.7%
preto 5518
 
2.7%
de 4192
 
2.1%
jose 4085
 
2.0%
santo 3270
 
1.6%
andre 3164
 
1.6%
Other values (640) 97267
47.9%
2025-07-05T18:24:36.415392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (31) 266265
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1195221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (31) 266265
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1195221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (31) 266265
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1195221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (31) 266265
22.3%

seller_state
Categorical

High correlation  Imbalance 

Distinct23
Distinct (%)< 0.1%
Missing833
Missing (%)0.7%
Memory size6.7 MiB
SP
84377 
MG
9314 
PR
9096 
RJ
 
5036
SC
 
4271
Other values (18)
 
6216

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236620
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 84377
70.8%
MG 9314
 
7.8%
PR 9096
 
7.6%
RJ 5036
 
4.2%
SC 4271
 
3.6%
RS 2294
 
1.9%
DF 949
 
0.8%
BA 700
 
0.6%
GO 550
 
0.5%
PE 465
 
0.4%
Other values (13) 1258
 
1.1%
(Missing) 833
 
0.7%

Length

2025-07-05T18:24:36.649524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 84377
71.3%
mg 9314
 
7.9%
pr 9096
 
7.7%
rj 5036
 
4.3%
sc 4271
 
3.6%
rs 2294
 
1.9%
df 949
 
0.8%
ba 700
 
0.6%
go 550
 
0.5%
pe 465
 
0.4%
Other values (13) 1258
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 236620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 236620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 236620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

geolocation_lat
Real number (ℝ)

High correlation 

Distinct2239
Distinct (%)1.9%
Missing1098
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean-22.797849
Minimum-36.605374
Maximum-2.5460792
Zeros0
Zeros (%)0.0%
Negative118045
Negative (%)99.1%
Memory size930.9 KiB
2025-07-05T18:24:36.852009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-36.605374
5-th percentile-26.322991
Q1-23.609026
median-23.422076
Q3-21.766477
95-th percentile-19.849883
Maximum-2.5460792
Range34.059295
Interquartile range (IQR)1.8425493

Descriptive statistics

Standard deviation2.6904204
Coefficient of variation (CV)-0.11801202
Kurtosis18.250517
Mean-22.797849
Median Absolute Deviation (MAD)0.53597732
Skewness2.7582692
Sum-2691172.1
Variance7.2383618
MonotonicityNot monotonic
2025-07-05T18:24:37.091407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-21.76647685 8373
 
7.0%
-23.65111484 2145
 
1.8%
-20.80243626 2098
 
1.8%
-23.66570346 1899
 
1.6%
-22.71683929 1678
 
1.4%
-23.48262344 1556
 
1.3%
-23.693986 1549
 
1.3%
-23.59798552 1477
 
1.2%
-23.62358155 1268
 
1.1%
-23.20706431 1255
 
1.1%
Other values (2229) 94747
79.5%
ValueCountFrequency (%)
-36.60537441 4
 
< 0.1%
-32.07951338 18
 
< 0.1%
-31.77241287 2
 
< 0.1%
-31.74423096 11
 
< 0.1%
-31.32151917 1
 
< 0.1%
-30.1594693 77
0.1%
-30.1100209 19
 
< 0.1%
-30.10330372 6
 
< 0.1%
-30.09981943 5
 
< 0.1%
-30.08061351 5
 
< 0.1%
ValueCountFrequency (%)
-2.546079234 410
0.3%
-3.135622683 3
 
< 0.1%
-3.719036598 14
 
< 0.1%
-3.723306129 3
 
< 0.1%
-3.723672447 1
 
< 0.1%
-3.743987899 4
 
< 0.1%
-3.759209507 1
 
< 0.1%
-3.769923 35
 
< 0.1%
-3.780360995 12
 
< 0.1%
-3.783814827 16
 
< 0.1%

geolocation_lng
Real number (ℝ)

High correlation 

Distinct2239
Distinct (%)1.9%
Missing1098
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean-47.238603
Minimum-67.809656
Maximum-34.847856
Zeros0
Zeros (%)0.0%
Negative118045
Negative (%)99.1%
Memory size930.9 KiB
2025-07-05T18:24:37.310147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-67.809656
5-th percentile-51.368949
Q1-48.831547
median-46.755211
Q3-46.518082
95-th percentile-43.297621
Maximum-34.847856
Range32.9618
Interquartile range (IQR)2.3134654

Descriptive statistics

Standard deviation2.3411036
Coefficient of variation (CV)-0.049559122
Kurtosis4.8442036
Mean-47.238603
Median Absolute Deviation (MAD)0.72882376
Skewness0.55253099
Sum-5576280.8
Variance5.4807663
MonotonicityNot monotonic
2025-07-05T18:24:37.535596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-48.83154738 8373
 
7.0%
-46.75521082 2145
 
1.8%
-49.39562407 2098
 
1.8%
-46.51808197 1899
 
1.6%
-47.65736585 1678
 
1.4%
-46.37448953 1556
 
1.3%
-46.70188324 1549
 
1.3%
-46.55547279 1477
 
1.2%
-46.61056007 1268
 
1.1%
-46.76073521 1255
 
1.1%
Other values (2229) 94747
79.5%
ValueCountFrequency (%)
-67.8096558 1
 
< 0.1%
-64.28394646 4
 
< 0.1%
-63.88797299 6
 
< 0.1%
-61.95720116 8
 
< 0.1%
-60.02346888 3
 
< 0.1%
-57.08632042 1
 
< 0.1%
-56.10296014 52
< 0.1%
-56.06797233 37
< 0.1%
-55.49644289 56
< 0.1%
-54.96989318 2
 
< 0.1%
ValueCountFrequency (%)
-34.84785618 1
 
< 0.1%
-34.8557625 9
 
< 0.1%
-34.89678629 3
 
< 0.1%
-34.89853108 16
 
< 0.1%
-34.90155285 1
 
< 0.1%
-34.91839939 6
 
< 0.1%
-34.93222646 386
0.3%
-35.12509238 16
 
< 0.1%
-35.20164321 23
 
< 0.1%
-35.20826027 16
 
< 0.1%
Distinct595
Distinct (%)0.5%
Missing1098
Missing (%)0.9%
Memory size7.9 MiB
2025-07-05T18:24:38.543679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length25
Median length23
Mean length10.075361
Min length3

Characters and Unicode

Total characters1189346
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)< 0.1%

Sample

1st rowmaua
2nd rowmaua
3rd rowmaua
4th rowbelo horizonte
5th rowguariba
ValueCountFrequency (%)
sao 30528
 
15.1%
paulo 29515
 
14.6%
ibitinga 8373
 
4.1%
são 5968
 
3.0%
rio 5905
 
2.9%
do 5604
 
2.8%
preto 5400
 
2.7%
de 3879
 
1.9%
jose 3675
 
1.8%
santo 3285
 
1.6%
Other values (639) 99814
49.4%
2025-07-05T18:24:39.901965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 191810
16.1%
o 144781
12.2%
i 100598
 
8.5%
83901
 
7.1%
r 77632
 
6.5%
s 75861
 
6.4%
u 63166
 
5.3%
e 62314
 
5.2%
p 57905
 
4.9%
l 56870
 
4.8%
Other values (25) 274508
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1189346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 191810
16.1%
o 144781
12.2%
i 100598
 
8.5%
83901
 
7.1%
r 77632
 
6.5%
s 75861
 
6.4%
u 63166
 
5.3%
e 62314
 
5.2%
p 57905
 
4.9%
l 56870
 
4.8%
Other values (25) 274508
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1189346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 191810
16.1%
o 144781
12.2%
i 100598
 
8.5%
83901
 
7.1%
r 77632
 
6.5%
s 75861
 
6.4%
u 63166
 
5.3%
e 62314
 
5.2%
p 57905
 
4.9%
l 56870
 
4.8%
Other values (25) 274508
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1189346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 191810
16.1%
o 144781
12.2%
i 100598
 
8.5%
83901
 
7.1%
r 77632
 
6.5%
s 75861
 
6.4%
u 63166
 
5.3%
e 62314
 
5.2%
p 57905
 
4.9%
l 56870
 
4.8%
Other values (25) 274508
23.1%

geolocation_state
Categorical

High correlation  Imbalance 

Distinct22
Distinct (%)< 0.1%
Missing1098
Missing (%)0.9%
Memory size6.7 MiB
SP
83572 
MG
9424 
PR
9212 
RJ
 
5204
SC
 
4459
Other values (17)
 
6174

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236090
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowMG
5th rowSP

Common Values

ValueCountFrequency (%)
SP 83572
70.1%
MG 9424
 
7.9%
PR 9212
 
7.7%
RJ 5204
 
4.4%
SC 4459
 
3.7%
RS 2298
 
1.9%
DF 908
 
0.8%
BA 704
 
0.6%
GO 550
 
0.5%
PE 465
 
0.4%
Other values (12) 1249
 
1.0%
(Missing) 1098
 
0.9%

Length

2025-07-05T18:24:40.200569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 83572
70.8%
mg 9424
 
8.0%
pr 9212
 
7.8%
rj 5204
 
4.4%
sc 4459
 
3.8%
rs 2298
 
1.9%
df 908
 
0.8%
ba 704
 
0.6%
go 550
 
0.5%
pe 465
 
0.4%
Other values (12) 1249
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 93305
39.5%
S 90790
38.5%
R 16782
 
7.1%
M 10044
 
4.3%
G 9974
 
4.2%
J 5204
 
2.2%
C 4563
 
1.9%
A 1118
 
0.5%
E 969
 
0.4%
D 908
 
0.4%
Other values (6) 2433
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 236090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 93305
39.5%
S 90790
38.5%
R 16782
 
7.1%
M 10044
 
4.3%
G 9974
 
4.2%
J 5204
 
2.2%
C 4563
 
1.9%
A 1118
 
0.5%
E 969
 
0.4%
D 908
 
0.4%
Other values (6) 2433
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 236090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 93305
39.5%
S 90790
38.5%
R 16782
 
7.1%
M 10044
 
4.3%
G 9974
 
4.2%
J 5204
 
2.2%
C 4563
 
1.9%
A 1118
 
0.5%
E 969
 
0.4%
D 908
 
0.4%
Other values (6) 2433
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 236090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 93305
39.5%
S 90790
38.5%
R 16782
 
7.1%
M 10044
 
4.3%
G 9974
 
4.2%
J 5204
 
2.2%
C 4563
 
1.9%
A 1118
 
0.5%
E 969
 
0.4%
D 908
 
0.4%
Other values (6) 2433
 
1.0%
Distinct71
Distinct (%)0.1%
Missing2567
Missing (%)2.2%
Memory size7.9 MiB
2025-07-05T18:24:40.859198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length39
Median length31
Mean length12.988299
Min length3

Characters and Unicode

Total characters1514124
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowhousewares
3rd rowhousewares
4th rowperfumery
5th rowauto
ValueCountFrequency (%)
bed_bath_table 11988
 
10.3%
health_beauty 10032
 
8.6%
sports_leisure 9004
 
7.7%
furniture_decor 8832
 
7.6%
computers_accessories 8150
 
7.0%
housewares 7380
 
6.3%
watches_gifts 6213
 
5.3%
telephony 4726
 
4.1%
garden_tools 4590
 
3.9%
auto 4400
 
3.8%
Other values (61) 41261
35.4%
2025-07-05T18:24:42.118237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 185298
12.2%
s 142057
 
9.4%
t 133341
 
8.8%
o 111892
 
7.4%
r 105808
 
7.0%
a 102466
 
6.8%
_ 102216
 
6.8%
u 78227
 
5.2%
c 72479
 
4.8%
i 63331
 
4.2%
Other values (15) 417009
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1514124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 185298
12.2%
s 142057
 
9.4%
t 133341
 
8.8%
o 111892
 
7.4%
r 105808
 
7.0%
a 102466
 
6.8%
_ 102216
 
6.8%
u 78227
 
5.2%
c 72479
 
4.8%
i 63331
 
4.2%
Other values (15) 417009
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1514124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 185298
12.2%
s 142057
 
9.4%
t 133341
 
8.8%
o 111892
 
7.4%
r 105808
 
7.0%
a 102466
 
6.8%
_ 102216
 
6.8%
u 78227
 
5.2%
c 72479
 
4.8%
i 63331
 
4.2%
Other values (15) 417009
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1514124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 185298
12.2%
s 142057
 
9.4%
t 133341
 
8.8%
o 111892
 
7.4%
r 105808
 
7.0%
a 102466
 
6.8%
_ 102216
 
6.8%
u 78227
 
5.2%
c 72479
 
4.8%
i 63331
 
4.2%
Other values (15) 417009
27.5%

Interactions

2025-07-05T18:23:52.639561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:22:57.612178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:00.210271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:02.831309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:05.474951image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:08.214689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:12.292889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:15.983571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:18.338781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:21.396287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:24.800285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:27.708427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:31.116811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:34.698529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:40.381237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:44.550035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:49.043502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:52.834250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:22:57.794618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:00.371629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:02.976743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:05.603419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:08.652255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:12.522271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:16.135726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:18.483812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:21.577964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:24.992811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:27.874377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:31.351449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:35.117017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:40.526596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:44.909747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:49.259636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:53.046694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:22:57.935803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:00.533631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:03.115171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:05.753773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:08.829952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:12.751795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:16.274203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:18.622059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:21.760388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:25.190399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:28.036293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:31.606414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:35.675666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:40.678249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-07-05T18:23:33.610361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:39.841464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:43.292926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:48.414471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:52.151735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:55.603874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:22:59.878041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:02.554765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:05.206457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:07.927741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:11.756506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:15.523566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:18.064272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:21.051304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:24.354393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:27.361743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:30.653468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:33.759574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:40.021087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:43.720528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:48.612239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:52.311874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:55.978330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:00.048614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:02.691564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:05.345477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:08.074215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:12.028630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:15.818649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:18.193365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:21.226375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:24.551904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:27.535325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:30.863254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:33.909936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:40.221916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:44.106163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:48.820382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-07-05T18:23:52.463420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-07-05T18:24:42.401034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
customer_statecustomer_zip_code_prefixfreight_valuegeolocation_latgeolocation_lnggeolocation_stateorder_item_idorder_statuspayment_installmentspayment_sequentialpayment_typepayment_valuepriceproduct_description_lenghtproduct_height_cmproduct_length_cmproduct_name_lenghtproduct_photos_qtyproduct_weight_gproduct_width_cmreview_scoreseller_statezip_code_prefix
customer_state1.0000.8960.0850.0510.0520.0560.0130.0260.0320.0260.0330.0290.0200.0260.0190.0170.0130.0140.0280.0160.0480.0530.069
customer_zip_code_prefix0.8961.0000.466-0.003-0.0090.066-0.0090.0220.069-0.0090.0290.1060.0700.0310.0190.0080.0150.0260.026-0.0020.0410.0650.060
freight_value0.0850.4661.0000.073-0.0650.048-0.0560.0150.1910.0170.0090.4230.4330.1170.2840.2840.0340.0110.4480.2750.0150.0480.257
geolocation_lat0.051-0.0030.0731.0000.0450.7540.0120.0080.0430.0110.0180.0630.065-0.083-0.0090.035-0.004-0.1310.0730.0960.0130.7500.273
geolocation_lng0.052-0.009-0.0650.0451.0000.882-0.0120.025-0.032-0.0030.015-0.045-0.0360.1300.009-0.069-0.0870.034-0.074-0.0740.0150.880-0.406
geolocation_state0.0560.0660.0480.7540.8821.0000.0000.0290.0330.0160.0200.0360.0520.1110.0660.0840.0690.0400.0780.0580.0230.9960.926
order_item_id0.013-0.009-0.0560.012-0.0120.0001.0000.0020.061-0.0080.0210.257-0.116-0.0320.0180.007-0.021-0.0650.000-0.0040.0410.000-0.011
order_status0.0260.0220.0150.0080.0250.0290.0021.0000.0050.0260.0370.0150.0140.0160.0160.0140.0190.0130.0110.0040.1490.0290.013
payment_installments0.0320.0690.1910.043-0.0320.0330.0610.0051.000-0.1780.2360.3950.3150.0330.1060.1090.016-0.0030.1980.1250.0270.0330.065
payment_sequential0.026-0.0090.0170.011-0.0030.016-0.0080.026-0.1781.0000.198-0.215-0.005-0.0130.0130.034-0.003-0.0050.0300.0280.0120.0160.007
payment_type0.0330.0290.0090.0180.0150.0200.0210.0370.2360.1981.0000.0180.0140.0200.0150.0220.0110.0040.0180.0200.0100.0210.018
payment_value0.0290.1060.4230.063-0.0450.0360.2570.0150.395-0.2150.0181.0000.7890.1690.3050.2290.025-0.0110.4490.2320.0280.0360.160
price0.0200.0700.4330.065-0.0360.052-0.1160.0140.315-0.0050.0140.7891.0000.2110.3270.2660.0420.0290.5140.2710.0120.0520.175
product_description_lenght0.0260.0310.117-0.0830.1300.111-0.0320.0160.033-0.0130.0200.1690.2111.0000.135-0.0210.0730.1110.095-0.0810.0150.1120.001
product_height_cm0.0190.0190.284-0.0090.0090.0660.0180.0160.1060.0130.0150.3050.3270.1351.0000.248-0.057-0.0790.5320.3380.0180.0650.049
product_length_cm0.0170.0080.2840.035-0.0690.0840.0070.0140.1090.0340.0220.2290.266-0.0210.2481.0000.0600.0050.6190.6320.0180.0840.067
product_name_lenght0.0130.0150.034-0.004-0.0870.069-0.0210.0190.016-0.0030.0110.0250.0420.073-0.0570.0601.0000.1630.0760.0650.0130.0700.009
product_photos_qty0.0140.0260.011-0.1310.0340.040-0.0650.013-0.003-0.0050.004-0.0110.0290.111-0.0790.0050.1631.0000.003-0.0150.0160.040-0.078
product_weight_g0.0280.0260.4480.073-0.0740.0780.0000.0110.1980.0300.0180.4490.5140.0950.5320.6190.0760.0031.0000.6210.0200.0780.096
product_width_cm0.016-0.0020.2750.096-0.0740.058-0.0040.0040.1250.0280.0200.2320.271-0.0810.3380.6320.065-0.0150.6211.0000.0140.0570.077
review_score0.0480.0410.0150.0130.0150.0230.0410.1490.0270.0120.0100.0280.0120.0150.0180.0180.0130.0160.0200.0141.0000.0230.021
seller_state0.0530.0650.0480.7500.8800.9960.0000.0290.0330.0160.0210.0360.0520.1120.0650.0840.0700.0400.0780.0570.0231.0000.920
zip_code_prefix0.0690.0600.2570.273-0.4060.926-0.0110.0130.0650.0070.0180.1600.1750.0010.0490.0670.009-0.0780.0960.0770.0210.9201.000

Missing values

2025-07-05T18:23:56.535720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-05T18:23:58.108543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-05T18:24:01.199986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

order_idcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_statepayment_sequentialpayment_typepayment_installmentspayment_valuereview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestamporder_item_idproduct_idseller_idshipping_limit_datepricefreight_valueproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmzip_code_prefixseller_cityseller_stategeolocation_latgeolocation_lnggeolocation_citygeolocation_stateproduct_category_name_english
0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:007c396fd4830fd04220f754e42b4e5bff3149sao pauloSP1.0credit_card1.018.12a54f0611adc9ed256b57ede6b6eb51144.0NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:481.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.72utilidades_domesticas40.0268.04.0500.019.08.013.09350.0mauaSP-23.680114-46.452454mauaSPhousewares
1e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:007c396fd4830fd04220f754e42b4e5bff3149sao pauloSP3.0voucher1.02.00a54f0611adc9ed256b57ede6b6eb51144.0NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:481.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.72utilidades_domesticas40.0268.04.0500.019.08.013.09350.0mauaSP-23.680114-46.452454mauaSPhousewares
2e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:007c396fd4830fd04220f754e42b4e5bff3149sao pauloSP2.0voucher1.018.59a54f0611adc9ed256b57ede6b6eb51144.0NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:481.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.72utilidades_domesticas40.0268.04.0500.019.08.013.09350.0mauaSP-23.680114-46.452454mauaSPhousewares
353cdb2fc8bc7dce0b6741e2150273451b0830fb4747a6c6d20dea0b8c802d7efdelivered2018-07-24 20:41:372018-07-26 03:24:272018-07-26 14:31:002018-08-07 15:27:452018-08-13 00:00:00af07308b275d755c9edb36a90c61823147813barreirasBA1.0boleto1.0141.468d5266042046a06655c8db133d120ba54.0Muito boa a lojaMuito bom o produto.2018-08-08 00:00:002018-08-08 18:37:501.0595fac2a385ac33a80bd5114aec74eb8289cdb325fb7e7f891c38608bf9e09622018-07-30 03:24:27118.7022.76perfumaria29.0178.01.0400.019.013.019.031570.0belo horizonteSP-19.810119-43.984727belo horizonteMGperfumery
447770eb9100c2d0c44946d9cf07ec65d41ce2a54c0b03bf3443c3d931a367089delivered2018-08-08 08:38:492018-08-08 08:55:232018-08-08 13:50:002018-08-17 18:06:292018-09-04 00:00:003a653a41f6f9fc3d2a113cf8398680e875265vianopolisGO1.0credit_card3.0179.12e73b67b67587f7644d5bd1a52deb1b015.0NaNNaN2018-08-18 00:00:002018-08-22 19:07:581.0aa4383b373c6aca5d8797843e55944154869f7a5dfa277a7dca6462dcf3b52b22018-08-13 08:55:23159.9019.22automotivo46.0232.01.0420.024.019.021.014840.0guaribaSP-21.362358-48.232976guaribaSPauto
5949d5b44dbf5de918fe9c16f97b45f8af88197465ea7920adcdbec7375364d82delivered2017-11-18 19:28:062017-11-18 19:45:592017-11-22 13:39:592017-12-02 00:28:422017-12-15 00:00:007c142cf63193a1473d2e66489a9ae97759296sao goncalo do amaranteRN1.0credit_card1.072.20359d03e676b3c069f62cadba8dd3f6e85.0NaNO produto foi exatamente o que eu esperava e estava descrito no site e chegou bem antes da data prevista.2017-12-03 00:00:002017-12-05 19:21:581.0d0b61bfb1de832b15ba9d266ca96e5b066922902710d126a0e7d26b0e38051062017-11-23 19:45:5945.0027.20pet_shop59.0468.03.0450.030.010.020.031842.0belo horizonteMG-19.840168-43.923299belo horizonteMGpet_shop
6ad21c59c0840e6cb83a9ceb5573f81598ab97904e6daea8866dbdbc4fb7aad2cdelivered2018-02-13 21:18:392018-02-13 22:20:292018-02-14 19:46:342018-02-16 18:17:022018-02-26 00:00:0072632f0f9dd73dfee390c9b22eb56dd69195santo andreSP1.0credit_card1.028.62e50934924e227544ba8246aeb3770dd45.0NaNNaN2018-02-17 00:00:002018-02-18 13:02:511.065266b2da20d04dbe00c5c2d3bb7859e2c9e548be18521d1c43cde1c582c6de82018-02-19 20:31:3719.908.72papelaria38.0316.04.0250.051.015.015.08752.0mogi das cruzesSP-23.551707-46.260979mogi das cruzesSPstationery
7a4591c265e18cb1dcee52889e2d8acc3503740e9ca751ccdda7ba28e9ab8f608delivered2017-07-09 21:57:052017-07-09 22:10:132017-07-11 14:58:042017-07-26 10:57:552017-08-01 00:00:0080bb27c7c16e8f973207a5086ab329e286320congonhinhasPR1.0credit_card6.0175.2689b738e70a1ce346db29a20fb29101614.0NaNNaN2017-07-27 00:00:002017-07-27 22:48:301.0060cb19345d90064d1015407193c233d8581055ce74af1daba164fdbd55a40de2017-07-13 22:10:13147.9027.36automotivo49.0608.01.07150.065.010.065.07112.0guarulhosSP-23.465304-46.511487guarulhosSPauto
8136cce7faa42fdb2cefd53fdc79a6098ed0271e0b7da060a393796590e7b737ainvoiced2017-04-11 12:22:082017-04-13 13:25:17NaNNaN2017-05-09 00:00:0036edbb3fb164b1f16485364b6fb04c7398900santa rosaRS1.0credit_card1.065.95e07549ef5311abcc92ba1784b093fb562.0NaNfiquei triste por n ter me atendido.2017-05-13 00:00:002017-05-13 20:25:421.0a1804276d9941ac0733cfd409f5206ebdc8798cbf453b7e0f98745e396cc56162017-04-19 13:25:1749.9016.05NaNNaNNaNNaN600.035.035.015.05455.0sao pauloSP-23.536892-46.713111sao pauloSPNaN
96514b8ad8028c9f2cc2374ded245783f9bdf08b4b3b52b5526ff42d37d47f222delivered2017-05-16 13:10:302017-05-16 13:22:112017-05-22 10:07:462017-05-26 12:55:512017-06-07 00:00:00932afa1e708222e5821dac9cd5db4cae26525nilopolisRJ1.0credit_card3.075.1607d67dd06ed5f88bef11ef6b464e79ae5.0NaNNaN2017-05-27 00:00:002017-05-28 02:59:571.04520766ec412348b8d4caa5e8a18c46416090f2ca825584b5a147ab24aa30c862017-05-22 13:22:1159.9915.17automotivo59.0956.01.050.016.016.017.012940.0atibaiaSP-23.112774-46.548885atibaiaSPauto
order_idcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_statepayment_sequentialpayment_typepayment_installmentspayment_valuereview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestamporder_item_idproduct_idseller_idshipping_limit_datepricefreight_valueproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmzip_code_prefixseller_cityseller_stategeolocation_latgeolocation_lnggeolocation_citygeolocation_stateproduct_category_name_english
1191339115830be804184b91f5c00f6f49f92dda2124f134f5dfbce9d06f29bdb6c308delivered2017-10-04 19:57:372017-10-04 20:07:142017-10-05 16:52:522017-10-20 20:25:452017-11-07 00:00:00c716cf2b5b86fb24257cffe9e7969df878048cuiabaMT2.0voucher1.064.37ebd75732b5804e934123d11ec1f11db05.0NaNNaN2017-10-21 00:00:002017-10-23 14:48:402.049d2e2460386273b195e7e59b43587c31caf283236cd69af44cbc09a0a1e7d322017-10-10 20:07:1426.9036.98brinquedos40.0180.03.0750.026.015.026.026020.0nova iguacuRJ-22.741390-43.447927nova iguaçuRJtoys
119134aa04ef5214580b06b10e2a378300db44f01a6bfcc730456317e4081fe0c9940edelivered2017-01-27 00:30:032017-01-27 01:05:252017-01-30 11:40:162017-02-07 13:15:252017-03-17 00:00:00e03dbdf5e56c96b106d8115ac336f47f35502divinopolisMG2.0voucher1.0250.00e8995c053d3db2d9c07407efe7de52dd5.0NaNNaN2017-02-08 00:00:002017-02-11 12:37:361.09fc063fd34fed29ccc57b7f8e8d03388ccc4bbb5f32a6ab2b7066a4130f114e32017-02-03 00:30:03370.0019.43beleza_saude48.0657.01.0750.038.012.025.080310.0curitibaPR-25.464029-49.304728curitibaPRhealth_beauty
119135aa04ef5214580b06b10e2a378300db44f01a6bfcc730456317e4081fe0c9940edelivered2017-01-27 00:30:032017-01-27 01:05:252017-01-30 11:40:162017-02-07 13:15:252017-03-17 00:00:00e03dbdf5e56c96b106d8115ac336f47f35502divinopolisMG1.0credit_card5.0139.43e8995c053d3db2d9c07407efe7de52dd5.0NaNNaN2017-02-08 00:00:002017-02-11 12:37:361.09fc063fd34fed29ccc57b7f8e8d03388ccc4bbb5f32a6ab2b7066a4130f114e32017-02-03 00:30:03370.0019.43beleza_saude48.0657.01.0750.038.012.025.080310.0curitibaPR-25.464029-49.304728curitibaPRhealth_beauty
119136880675dff2150932f1601e1c07eadeeb47cd45a6ac7b9fb16537df2ccffeb5acdelivered2017-02-23 09:05:122017-02-23 09:15:112017-03-01 10:22:522017-03-06 11:08:082017-03-22 00:00:00831ce3f1bacbd424fc4e38fbd4d66d295127sao pauloSP1.0credit_card3.0155.9996e8f371a3983122b739944537e155255.0NaNNaN2017-03-07 00:00:002017-03-11 15:42:411.0ea73128566d1b082e5101ce46f8107c7391fc6631aebcf3004804e51b40bcf1e2017-02-27 09:05:12139.9016.09moveis_decoracao63.0254.02.02500.049.013.041.014940.0ibitingaSP-21.766477-48.831547ibitingaSPfurniture_decor
1191379c5dedf39a927c1b2549525ed64a053c39bd1228ee8140590ac3aca26f2dfe00delivered2017-03-09 09:54:052017-03-09 09:54:052017-03-10 11:18:032017-03-17 15:08:012017-03-28 00:00:006359f309b166b0196dbf7ad2ac62bb5a12209sao jose dos camposSP1.0credit_card3.085.08e262b3f92d1ce917aa412a9406cf61a65.0NaNNaN2017-03-22 00:00:002017-03-23 11:02:081.0ac35486adb7b02598c182c2ff2e05254e24fc9fcd865784fb25705606fe3dfe72017-03-15 09:54:0572.0013.08beleza_saude50.01517.01.01175.022.013.018.012913.0braganca paulistaSP-22.957505-46.524886braganca paulistaSPhealth_beauty
11913863943bddc261676b46f01ca7ac2f7bd81fca14ff2861355f6e5f14306ff977a7delivered2018-02-06 12:58:582018-02-06 13:10:372018-02-07 23:22:422018-02-28 17:37:562018-03-02 00:00:00da62f9e57a76d978d02ab5362c50966011722praia grandeSP1.0credit_card3.0195.0029bb71b2760d0f876dfa178a76bc47344.0NaNSo uma peça que veio rachado mas tudo bem rs2018-03-01 00:00:002018-03-02 17:50:011.0f1d4ce8c6dd66c47bbaa8c6781c2a9231f9ab4708f3056ede07124aad39a25542018-02-12 13:10:37174.9020.10bebes52.0828.04.04950.040.010.040.017602.0tupaSP-21.935321-50.497562tupaSPbaby
11913983c1379a015df1e13d02aae0204711ab1aa71eb042121263aafbe80c1b562c9cdelivered2017-08-27 14:46:432017-08-27 15:04:162017-08-28 20:52:262017-09-21 11:24:172017-09-27 00:00:00737520a9aad80b3fbbdad19b66b37b3045920nova vicosaBA1.0credit_card5.0271.01371579771219f6db2d830d50805977bb5.0NaNFoi entregue antes do prazo.2017-09-22 00:00:002017-09-22 23:10:571.0b80910977a37536adeddd63663f916add50d79cb34e38265a8649c383dcffd482017-09-05 15:04:16205.9965.02eletrodomesticos_251.0500.02.013300.032.090.022.08290.0sao pauloSP-23.551013-46.448489sao pauloSPhome_appliances_2
11914011c177c8e97725db2631073c19f07b62b331b74b18dc79bcdf6532d51e1637c1delivered2018-01-08 21:28:272018-01-08 21:36:212018-01-12 15:35:032018-01-25 23:32:542018-02-15 00:00:005097a5312c8b157bb7be58ae360ef43c28685japuibaRJ1.0credit_card4.0441.168ab6855b9fe9b812cd03a480a25058a12.0NaNFoi entregue somente 1. Quero saber do outro produto.2018-01-26 00:00:002018-01-27 09:16:561.0d1c427060a0f73f6b889a5c7c61f2ac4a1043bafd471dff536d0c462352beb482018-01-12 21:36:21179.9940.59informatica_acessorios59.01893.01.06550.020.020.020.037175.0ilicineaMG-20.944706-45.827098ilicineaMGcomputers_accessories
11914111c177c8e97725db2631073c19f07b62b331b74b18dc79bcdf6532d51e1637c1delivered2018-01-08 21:28:272018-01-08 21:36:212018-01-12 15:35:032018-01-25 23:32:542018-02-15 00:00:005097a5312c8b157bb7be58ae360ef43c28685japuibaRJ1.0credit_card4.0441.168ab6855b9fe9b812cd03a480a25058a12.0NaNFoi entregue somente 1. Quero saber do outro produto.2018-01-26 00:00:002018-01-27 09:16:562.0d1c427060a0f73f6b889a5c7c61f2ac4a1043bafd471dff536d0c462352beb482018-01-12 21:36:21179.9940.59informatica_acessorios59.01893.01.06550.020.020.020.037175.0ilicineaMG-20.944706-45.827098ilicineaMGcomputers_accessories
11914266dea50a8b16d9b4dee7af250b4be1a5edb027a75a1449115f6b43211ae02a24delivered2018-03-08 20:57:302018-03-09 11:20:282018-03-09 22:11:592018-03-16 13:08:302018-04-03 00:00:0060350aa974b26ff12caad89e55993bd683750lapaPR1.0debit_card1.086.86dc9c59b4688062c25758c2be4cafc5235.0NaNNaN2018-03-17 00:00:002018-03-17 16:33:311.0006619bbed68b000c8ba3f8725d5409eececbfcff9804a2d6b40f589df8eef2b2018-03-15 10:55:4268.5018.36beleza_saude45.0569.01.0150.016.07.015.014407.0francaSP-20.502342-47.421590francaSPhealth_beauty